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PEMODELAN PREDIKSI KINERJA SISWA DENGAN HYBRID PARTICLE SWARM OPTIMIZATION-SUPPORT VECTOR REGRESSION (PSO-SVR) DAN GENETIC ALGORITHM-SUPPORT VECTOR REGRESSION (GA-SVR)

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Improving the quality of education is very important in the development of a country. Identifying representative educational data and conducting predictive analysis of student performance in an educational institution is very important to assist management in determining strategies and making decisions to improve student performance. This study modeled student performance using Hybrid Particle Swarm Optimization-Support Vector Regression (PSO-SVR) and Genetic Algorithm-Support Vector Regression (GA-SVR). This study uses predictive modeling of student performance with SVR modeling. PSO and GA are used as feature selections to identify relevant factors that influence student performance. The feature selection results, which are selected features, are then predicted with SVR to produce FSPSO-SVR and FSGA-SVR modeling. This study also performs hyperparameter optimization on SVR to obtain the best hyperparameters using PSO and GA, resulting in PSO-SVR (PSVR) and GA-SVR (GSVR) modeling. In the final stage, a comparison of the results of regression performance metrics was carried out from this study and previous studies. From the results of this study, the SVR modeling produced an RMSE value of 4.519; the FSPSO-SVR modeling produced an RMSE value of 2.954; the FSGA-SVR modeling produced an RMSE value of 4.447; the PSVR modeling resulted in an RMSE value of 1.608; the GSVR modeling had an RMSE value of 1.830. Meanwhile, from previous research conducted by Cortez on SVR modeling, the RMSE value was 2.090. The comparison of regression performance metrics found that the best student performance prediction modeling used PSVR modeling with a decrease in error values of 64.42% compared to SVR modeling in this study and a decrease in error values of 23.06% compared to previous studies.

Availability
Inventory Code Barcode Call Number Location Status
2307001742T101228T1012282023Central Library (Referens)Available
Detail Information
Series Title
-
Call Number
T1012282023
Publisher
Palembang : Prodi Ilmu Teknik Program Dokter, Fakultas Teknik Universitas Sriwijaya.,
Collation
xiv, 83 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
620.001 07
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Ilmu Teknik Program Dokter
Teori Ilmu Teknik-Prediksi Kinerja
Specific Detail Info
-
Statement of Responsibility
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No other version available

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